Building on the Past: Enacting Established Personal Identities in a New Work Setting
Bibliographic record
Abstract
A qualitative, longitudinal study of two groups of experienced professionals beginning work in a research organization provided insights into how newcomers with work experience adjust to and become assimilated into new jobs and work settings. Multiple methods were used to collect data on the newcomers' work experiences before and after assuming their new jobs. Repeated interviews with them during their first six months in their new jobs revealed that their past experience affected their assimilation in three primary ways: through the personal identities they had developed and carried with them, through the know-how they had acquired in past jobs and how well it fit with their new jobs, and through the personal tactics they had learned for managing their work and managing change. In general, newcomers with diverse experience adjusted better than those with narrow experience because (1) they found it easier to enact dimensions of their personal identities that allowed them to function effectively in the new situation, (2) they more easily found a fit between know-how gleaned from that experience and their new jobs, and (3) they could draw on a wider variety of personal tactics that they had previously used to help them adjust.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".